{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 在旧金山做不同工作工资数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline  \n",
    "plt.style.use('fivethirtyeight')\n",
    "#导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_ranges = pd.read_csv('~/开发/AI相关/特征工程/Feature-Engineering-Made-Easy-master/data/Salary_Ranges_by_Job_Classification.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SetID</th>\n",
       "      <th>Job Code</th>\n",
       "      <th>Eff Date</th>\n",
       "      <th>Sal End Date</th>\n",
       "      <th>Salary SetID</th>\n",
       "      <th>Sal Plan</th>\n",
       "      <th>Grade</th>\n",
       "      <th>Step</th>\n",
       "      <th>Biweekly High Rate</th>\n",
       "      <th>Biweekly Low Rate</th>\n",
       "      <th>Union Code</th>\n",
       "      <th>Extended Step</th>\n",
       "      <th>Pay Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COMMN</td>\n",
       "      <td>0109</td>\n",
       "      <td>07/01/2009 12:00:00 AM</td>\n",
       "      <td>06/30/2010 12:00:00 AM</td>\n",
       "      <td>COMMN</td>\n",
       "      <td>SFM</td>\n",
       "      <td>00000</td>\n",
       "      <td>1</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>$0.00</td>\n",
       "      <td>330</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMMN</td>\n",
       "      <td>0110</td>\n",
       "      <td>07/01/2009 12:00:00 AM</td>\n",
       "      <td>06/30/2010 12:00:00 AM</td>\n",
       "      <td>COMMN</td>\n",
       "      <td>SFM</td>\n",
       "      <td>00000</td>\n",
       "      <td>1</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COMMN</td>\n",
       "      <td>0111</td>\n",
       "      <td>07/01/2009 12:00:00 AM</td>\n",
       "      <td>06/30/2010 12:00:00 AM</td>\n",
       "      <td>COMMN</td>\n",
       "      <td>SFM</td>\n",
       "      <td>00000</td>\n",
       "      <td>1</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>$25.00</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COMMN</td>\n",
       "      <td>0112</td>\n",
       "      <td>07/01/2009 12:00:00 AM</td>\n",
       "      <td>06/30/2010 12:00:00 AM</td>\n",
       "      <td>COMMN</td>\n",
       "      <td>SFM</td>\n",
       "      <td>00000</td>\n",
       "      <td>1</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>$50.00</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COMMN</td>\n",
       "      <td>0114</td>\n",
       "      <td>07/01/2009 12:00:00 AM</td>\n",
       "      <td>06/30/2010 12:00:00 AM</td>\n",
       "      <td>COMMN</td>\n",
       "      <td>SFM</td>\n",
       "      <td>00000</td>\n",
       "      <td>1</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>$100.00</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SetID Job Code                Eff Date            Sal End Date  \\\n",
       "0  COMMN     0109  07/01/2009 12:00:00 AM  06/30/2010 12:00:00 AM   \n",
       "1  COMMN     0110  07/01/2009 12:00:00 AM  06/30/2010 12:00:00 AM   \n",
       "2  COMMN     0111  07/01/2009 12:00:00 AM  06/30/2010 12:00:00 AM   \n",
       "3  COMMN     0112  07/01/2009 12:00:00 AM  06/30/2010 12:00:00 AM   \n",
       "4  COMMN     0114  07/01/2009 12:00:00 AM  06/30/2010 12:00:00 AM   \n",
       "\n",
       "  Salary SetID Sal Plan  Grade  Step Biweekly High Rate Biweekly Low Rate  \\\n",
       "0        COMMN      SFM  00000     1              $0.00             $0.00   \n",
       "1        COMMN      SFM  00000     1             $15.00            $15.00   \n",
       "2        COMMN      SFM  00000     1             $25.00            $25.00   \n",
       "3        COMMN      SFM  00000     1             $50.00            $50.00   \n",
       "4        COMMN      SFM  00000     1            $100.00           $100.00   \n",
       "\n",
       "   Union Code  Extended Step Pay Type  \n",
       "0         330              0        C  \n",
       "1         323              0        D  \n",
       "2         323              0        D  \n",
       "3         323              0        D  \n",
       "4         323              0        M  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看前几行数据\n",
    "salary_ranges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1356 entries, 0 to 1355\n",
      "Data columns (total 13 columns):\n",
      "SetID                 1356 non-null object\n",
      "Job Code              1356 non-null object\n",
      "Eff Date              1356 non-null object\n",
      "Sal End Date          1356 non-null object\n",
      "Salary SetID          1356 non-null object\n",
      "Sal Plan              1356 non-null object\n",
      "Grade                 1356 non-null object\n",
      "Step                  1356 non-null int64\n",
      "Biweekly High Rate    1356 non-null object\n",
      "Biweekly Low Rate     1356 non-null object\n",
      "Union Code            1356 non-null int64\n",
      "Extended Step         1356 non-null int64\n",
      "Pay Type              1356 non-null object\n",
      "dtypes: int64(3), object(10)\n",
      "memory usage: 137.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据有多少行，是否有缺失值，以及每列的数据类型\n",
    "salary_ranges.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SetID                 0\n",
       "Job Code              0\n",
       "Eff Date              0\n",
       "Sal End Date          0\n",
       "Salary SetID          0\n",
       "Sal Plan              0\n",
       "Grade                 0\n",
       "Step                  0\n",
       "Biweekly High Rate    0\n",
       "Biweekly Low Rate     0\n",
       "Union Code            0\n",
       "Extended Step         0\n",
       "Pay Type              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算缺失值\n",
    "salary_ranges.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Step</th>\n",
       "      <th>Union Code</th>\n",
       "      <th>Extended Step</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1356.000000</td>\n",
       "      <td>1356.000000</td>\n",
       "      <td>1356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.294985</td>\n",
       "      <td>392.676991</td>\n",
       "      <td>0.150442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.045816</td>\n",
       "      <td>338.100562</td>\n",
       "      <td>1.006734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>790.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Step   Union Code  Extended Step\n",
       "count  1356.000000  1356.000000    1356.000000\n",
       "mean      1.294985   392.676991       0.150442\n",
       "std       1.045816   338.100562       1.006734\n",
       "min       1.000000     1.000000       0.000000\n",
       "25%       1.000000    21.000000       0.000000\n",
       "50%       1.000000   351.000000       0.000000\n",
       "75%       1.000000   790.000000       0.000000\n",
       "max       5.000000   990.000000      11.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看一些定量数据的描述性统计，describe方法默认描述定量列，没有定量列，也会描述定性列\n",
    "salary_ranges.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pands认为，数据只有三个定量列:Step，Union Code和Extended Step。 但是很明显其中的Union Code就不是定量数据，只不过是部门的代码而已。在这个数据当中我们感兴趣的是一个定量列Biweekly High Rate和一个定性列Grade。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Biweekly High Rate</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$0.00</td>\n",
       "      <td>00000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$15.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$25.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$50.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$100.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Biweekly High Rate  Grade\n",
       "0              $0.00  00000\n",
       "1             $15.00  00000\n",
       "2             $25.00  00000\n",
       "3             $50.00  00000\n",
       "4            $100.00  00000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_ranges = salary_ranges[['Biweekly High Rate', 'Grade']]\n",
    "salary_ranges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Biweekly High Rate</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.00</td>\n",
       "      <td>00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Biweekly High Rate  Grade\n",
       "0               0.00  00000\n",
       "1              15.00  00000\n",
       "2              25.00  00000\n",
       "3              50.00  00000\n",
       "4             100.00  00000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#清理一下数据，移除工资前面的美元符号。\n",
    "#处理定量数据一般使用整数或浮点数作为数据类型\n",
    "#处理定性数据一般使用字符串或Unicode对象\n",
    "salary_ranges['Biweekly High Rate'] = salary_ranges['Biweekly High Rate'].map(lambda value: value.replace('$', ''))\n",
    "salary_ranges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1356 entries, 0 to 1355\n",
      "Data columns (total 2 columns):\n",
      "Biweekly High Rate    1356 non-null float64\n",
      "Grade                 1356 non-null object\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 21.3+ KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Users/super/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#将Biweekly High Rate转换为浮点数\n",
    "salary_ranges['Biweekly High Rate'] = salary_ranges['Biweekly High Rate'].astype(float)\n",
    "#将Grade转换为字符串类型\n",
    "salary_ranges['Grade'] = salary_ranges['Grade'].astype(str)\n",
    "salary_ranges.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们有两列数据，BiWeekly High Rate代表双周最高工资，是定量列，代表某个部门的最高工资，Grade是工资所对应的部门"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "00000    61\n",
       "07450    12\n",
       "07170     9\n",
       "06870     9\n",
       "07420     9\n",
       "Name: Grade, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对Grade进行分类统计\n",
    "salary_ranges['Grade'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16baadd8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制条形图进行显示，定类等级数据\n",
    "salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(20).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16c90a20>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zqff34uJizJkzB4MHD8ZTTz2FAQMGAAByc3OxZcsW5OTk4JtvvjFftQYoUrEUTMTmKett/+zAqAviDz/8MHQ6HT777LMOH1+wYAHEYjGSkpJMVqCxLlY0YfTeEt7enxBLGOYtxU/TfPkuw6yMusWWnJyMBx54oNPHx40bh+Tk5N7W1Ctqlo6aiO2zh2tORoWTWCzGpUuXOn38woULvA8poHAi9qC0UQeNjX/WjUqS6dOnY8eOHdi4cSNqa/8YR1RbW4u3334bn3/+OWbMmGHyIo1h6/9ghLRotPHPulHXnGprazF//nycPHkSYrEYvr6+YBgGxcXFYFkWo0aNwldffQVXV1dz1tylw8oG/OUov3cLCbGE63/1h6eDmO8yzMaoQZiurq7Yt28fDh06hB9//BFKpRIcx2H8+PEYP348Jk2aZK46Daa2/VNxQgAATTY+g6VHI8QnTZokiCDqCJ3WEXuhsfGOmMKZEGciahv/ByOkha2vTm70kVN2djY++ugjZGVlobq6Gjqd/neIYRhkZWWZrEBjaei0zqKk0GGP04+4rVBAU6VrN3OAmI87NxGAG99lmI1R4ZSWloaZM2fCxcUFw4YNw/nz5zFmzBio1Wqkp6dj8ODBiI6ONletBqGhBJbTh2lA+s0tcAlW46DEB0qZHG6aUNTW1PFdml2QMHxXYF5GndatX78egYGByMjIwL///W8AwLJly3D48GEcOnQIhYWFmD17tlkKNZStn4cLRbyoBDmX18Ev9yxYDxmGi7Nxu+4ashuPwiPQAWKxzV0xEJyWea62yqhPUGZmJhYuXAiFQtE62LLltC4uLg4PP/wwXn31VdNXaQSRbf97CcIyUQ4On1kDhxIlAIB1ZuCkq0SUfzBYTovzpUdR63oDHl62PTGVb3wPeDY3o746hmHg7t78gXNycgIAvQ4EAwYMwNWrV01YnvF8HW133IcQfKFNxpsnXoVI9ccgXJ2seY3AEdIbEDHNH6myeiUuVB+ESwADmcy2m6LxRS6X812CWRkVTiEhIbh+/TqA5m9MaGio3ly61NRUeHp6mrZCI/k52vZPE744Qousqs+QeGorGFb/rgPLNF9jUuiKcLd/aOt2juFwpewkiqXn4elLR1GmJBaLIZH0qlek4Bn1P3ncuHHYu3dv6x2Zhx9+GF988QWmT5+OadOmYdeuXZgzZ45ZCjWUjw2PmOVLf6YeBb9tRETW4Q4f57R/tNYZ6Xi73eO1mnJkVX4PqZ8KTs5OZqvTntj6URNg5PSVqqoq5OfnIzw8HFKpFBzH4d1338WePXsgFosxadIkLFu2DFKp1Jw1d6mskcWArzpfW48YZ6roNr6+sBGysvahAwBaHwXKJzfqbfu/xljklhR0uL9c7IghHmNRWVRPww56wcPDg/ebT+bWqwUOhIjjOPgk3YLWpr4qfvwvcwlrTm+CqFHV6T6NUXehOlo/uG5KBuHz612v/NzHbRA8tP1RU00LUfSEv78/pk2bxncZZmXwaV1DQwM8PT3x9ttvm7OeXmMYBt4OdN2pt/ZofsTa4292GUwAoPVxabctSPsrgj37dPm8wppfcUX1IxSBcojFdCpuLHs4rTP4f7GjoyN8fHzg5ib8Eal0x67nXJkmXKnYjmmpSWC47udH6BQdn8Lfq+h+yWwdWFwoPYZql1x4eNMFc2M4OzvzXYLZGXWI8ec//xl79uxpN2VFaOiOXc/cLapBft6bGHThmMHPYTv5P3JX02X4uRvWRrZCdQvnq7+HU4AOcjkNOzCENRwk9JZR9yKnTJmClJQUTJw4EQsXLkTfvn3h6OjYbr+YmBiTFdgTPnTkZLQ5opvYkfk2pJXGLePOSjVAU/vtDMNhpJcUe7u+9KQnuywVzjIFBilGobzYiCfaIQqnNqZPn97654yMjHbD54WyNBQdORnndWRixakPwGgau9+5DR3T+QXtMG0WPJzDUFlv+OehXlOFzIrv0c83GvJ6X9TXd33Ny161DIa2ZUaF04cffmiuOkwq2MW2B6eZCsNxOKz+HvFpO8H04La+jmH0xji1JYIOcb6uOHzD+B9W1yuzIBU7IDxgLCqLVDTs4A4Mw/DabdZSjPpfPG/ePHPVYVJDPfkbZ2UtvBgN0ku2I/TKyR6/BuurAHQNXe4TwWbhlEMI6hqNHzLQxDYiq+wHBHj0h5duEGqqaNgBALi4uNjFHU6bPP8J95RATBOAOxXDVCEv99VeBRMAsIHdT1WSQIMR/t69ep/btXm4Uv8D3ANlNj9lwxD2cEoHGHjktH37dvj5+WHKlCkAgJqaGsyfP7/dfiEhIYI49XOSiDDQXYLsKttfFdVYj4hu4KNf3oGkpvfXBbXeht3OjsJFpEq9oW4y/ppWCx10uFj6EzycAhAqH4aKUvu9YO7t3buwtxbdHjkdOHAAK1as0EtrrVaLU6dO4caNGyguLkZxcTGKiorw1Vdf4YcffjBrwYaK9KJTu7Y2cen4OGWdSYIJANhOxji15aCrR4x/oEnes7LhNrKqvoejvxZyB9sfiNgRX1/bXum3RbfhtHv3bsTExOD+++9v99iHH36I9PR0pKenIyMjAzExMdi1a5dZCjVWtBeNl2khhg4n6r/FkhObwGg7uO/fQ6yT4Reph4uzIRGb7gdGTvkZFIoz4OVv+7fU26Jw+t3Zs2fxpz/9yaAXmzBhAjIyMnpdlCmM8KFwAoAAphE3bn+AURn/Z/LX1knVBu/b0ozOlFSaGmSWH4TIpxouLrY/YhpoXp6to7GFtqjbcCopKUGfPvrzpBwcHPD4448jKChIb7u/vz9KS40bxGcu0d5SONl6k+VujBKVIyf7FQTmpJnl9bsa49SRO5vRmVJ+1UX82pQMz0Bnm+8OaS9HTYAB4SSXy9HQoH+72MnJCRs2bED//v31tjc0NPDaLuVOUhGDGG9h1MKHv4ty8VP6ajjdzjfL6+sYEbgm465dtW1GZ0panQZZpT+gUaGEu4ftnur5+PjwXYLFdBtOffv2NfhULSMjA3379u1tTSYz0s8+L5huY09h04n1ENeZ744WG+ABcMbfDe2oGZ0pFdXewKW6Q3ALkAjmB6UpBQQEGLzv1q1bERkZCT8/P4wdOxapqakAgIKCAigUig5/vf/+++1eh+M4PPTQQ1AoFNi7d2/r9oKCAixduhRRUVHw9/dHVFQU1q1b1+5gRqlUIjExEYGBgejXrx9WrlwJjab7ieHdhtOECROwd+9e5OTkdLlfdnY29u7dK6iVgO/zs6/rTlLokF67Ew+f3AKGNe8wCjbAo0fP89YWYKCveY6eWnDgcKnsOMocLsPTx3bGBDk6OsLLy8ugfb/99lusWrUKy5cvR0pKCmJjYzFnzhwolUoEBQUhJydH79fGjRvBMIzeFLUWH3zwQYeDPnNzc8GyLN555x2cOXMGb731Fnbu3IlVq1a17sOyLBITE1FXV4eDBw9i27Zt2LdvH1566aVuv4Zum81VVVUhLi4ODMPgtddew/Tp0/UGwmm1Wnz33Xd46aWXIBKJkJqaCg+Pnn1wTa2+SYeBO4ugsoPOc8GMCunKLfC5ds4i71c3IRL1/r/26LmGNKMzpUFecUCVKxobez7OSggGDBiAcePGGbRvQkICwsPD9Y6Ehg0bhhkzZmDt2rXt9p85cyYYhsGePXv0tmdmZmLBggU4fvw4Bg4ciKSkJMyYMaPT9926dSteffVV3LhxAwBw5MgRzJ07FxcvXmy9Rr1r1y4888wzyM3N7XICc7dHTgqFAl9//TXEYjEef/xxhIaGYsyYMZg8eTLGjBmDkJAQLF68GFKpFDt37hRMMAGAs1SE8UEOfJdhdn8SleDq5XUWCyYAYN17fspkSDM6U/q1PA1KUZrVDzsIDjbsbqdGo0FWVhbi4+P1tsfHxyMtrf3Nkfz8fJw4cQKPPPKI3vba2lo89thjePfddw2+1lVbWwuFQtH69/T0dISFhendPEtISIBare52ZXCDbm1ERUXh9OnTWLt2LSIjI6FUKpGeng6lUomoqCi8/PLLSE1NRVRUlEFfgCU91M+2b7u+wFzF92dWw6HkpkXf15gxTh0xpBmdKTU01SKz/CDgUwkXV+sbdsAwjMHhVF5eDpZl2wWKj48PSkpK2u2/Y8cOeHl5YfLkyXrbly1bhoSEBIwfP96g91Uqldi8eTMee+yx1m0lJSXt6vDy8oJYLO6wljsZPFHJzc0Nzz77LJ599llDnyII44Mc4CZlUNNke6d2XzX9hNmnPwHDQ/M/nbQR6EW+NDeji0BxddcfUFP7reoyxMyviAh8ANVFasE3Tmzh7+9vdGvezloa3Umr1eLLL7/EvHnz9G4g7Ny5E5cuXdJb+q0rJSUleOihhzBu3DgsWbKkyzq6297CtgeFAJCLGUwOsa1TO0docb5qB+b8vI2XYAIAHde7DgEtzej4wHJNOF96BCq3Aig8reNUz5i74J0dmZSVlbU7ijl06BCKioqwcOFCve0nTpxAdnY2+vTpAy8vr9YL8YsWLcLEiRP19i0uLsa0adMwZMgQ/Oc//9ELHV9f33Z1dHZk15bNhxMAPNTPdtZKG8jUoaDgbYRn8TeHUScWgdP2fn5eczM6/hZhLakvwMWaQ3ANEAl62IFIJGo3prArMpkM0dHR7Y56kpOTERcXp7dtx44dGDVqFAYMGKC3ffXq1fj5559x8uTJ1l8A8Morr+Cjjz5q3a+oqAhTp07FoEGDsG3btnZdI2JjY5GTk4PCwkK9OuRyOaKjo7v8Ouyi/8S4QDm85CKUq63jEL4z00W3sPP8RsjK+V2Xjw3wAnp55AT0rhmdqXAMh8tlKXBz8EY/jzhUlAiv20FQUJDRU1aWLFmCJ554AjExMYiLi8P27dtRVFSERYsWte6jVCpx7NgxvbBpERgYiMDA9pO1g4KCWo/ibt++jalTp8Lf3x+vv/46ysvLW/fz9vaGWCxGfHw8hgwZgieffBLr169HZWUl1qxZg4ULF3bbatguwkkiYjC9rwM+ybHelq9rcREvpb7f7VJNlsAGKACYpvFbb5rRmVJNYxmyGr/HAL8RENco2g0k5NOgQYOMfs6sWbNQUVGBDRs2oLi4GEOGDMHXX3+NkJCQ1n0+++wzuLm5dTi2yRA//fQT8vLykJeXh4iICL3Hzp8/j9DQUIjFYuzatQsrVqzAxIkT4eDggNmzZ2P9+vXdvr7NLarZmZO31Zh2uIzvMnpkn/owJp35wqClmiyhblIU6n27HpRrjDTRSCTn3zDZ6/WWg8QZQxRjUFFUx3t7YLlcjvnz59tF58u27OKaEwCM8pchwMm6vlx3aJBdvhWTT38mmGACANbNtAfcUbgIuVQ4Ny0atfXILDsEnVcZXN3aLxpqSf369bPLYALsKJxEDIM/32U9Y54iRDW4cf1NDLho2K1cS2KdTBuUpmxGZ0rK6qvIbjwKRaADxGJ+/quEhYXx8r5CYDfhBACLh7hYRW/xRJESGefWwO23bL5L6ZBOYvppIDEmbkZnKiynxYXSo6h1vQEPL8vO0/Pz87OrLgRt2VU49XWVCH7E+FvcOXx+6mVIq4TRF6sjOq7G5K/prKtEVEBI9zvypKxeiQvVB+ESwEAms8yE8qFDh1rkfYTKrsIJAJZHukIkwKMnhuNwpGEflp3Y2KPFLS1FJxGDa+p8rbreGCG5bpZmdKbCMRyulJ1EsfQ8PH3NexTl5uYmqPZDfBDuJ8FMwhRSTA8V1tGTF6NBXukWjEsTRv/1rrCBXgDMc3HenM3oTKlWU46syu8h9VPByck8n6WhQ4d2O73D1tldOAHAiihXCOWffYSoEnm/rkfIlZ/5LsUgWn9F9zv1grmb0ZlSXsU53NCdgmeAq0mDRC6X92hsk62xy3CK8JRiQjD/t64fFd3AyV9Ww6Uwj+9SDMZ6m3cqkCWa0ZmSmm1AVtkhaD2L4eZumiXCw8PDafFQ2Gk4AcALUfyuNf+h7gz+k7IOkhrzXL8xF62b+cfcxLlUmf09TK2w5ldcUf0IRaC8V+OSHBwc7P5CeAu7DacYHxnGBVq+x7gYOpyq240nUjabdA05S9E5mn8wqKWb0ZmKDiwulB5Djcs1eHj37IJ5dHS0xe4GCp3dhtSwQGAAABLlSURBVBPQfO3JkvowDci/vRkjf9nT/c4CpZNYZs6ZpZvRmVK5qhDnq7+HU4AOcrnhQePi4oK7777bjJVZF7sOp1H+costgjBaVIbsq68gICfdIu9nLjrOMrP2m5vRWfcabdllqbglOQcvP8OOooYPH263U1U6YtfhBABvxLnD3GtvLmVycTRtNRyLCsz7Rmamk0nBNVkmnPhsRmdK9ZoqZFZ8D4lvHZydO7+Z4OXl1a6nkr2z+3CK9JLh6QjzTe78hD2Jd1PWQ1xv+lHVlsb2Md8Yp47w3YzOlK5XZuEamwLPQJcOhx20rHBE/mD34QQA/4h2wwATz7SXcyx+qfkSfzv5kdnXkLOUJn/Lzi1raUZnK5rYRmSVHobG4zbcFH98Xf3790efPtZ3A8DcKJwAOEgYvDdKYbKBmaFMPQpuvoPoc9+b6BWFgfW0/Mj6CDYLLg62E1AAcLv2Gq7U/wD3QBmcnJwwcuRIvksSJAqn393vL8fDg3o/wHCCqBhXLq2Dd17Xa3JZI1P3cTKEBBqM8Pe2+Puamw46XCz9CWHDg+DkZDs97k2JwukO60a496oh3T+YK9h/ejXkpYXd72yFdI78nJ4KrRmdqdzlPxgxg8bwXYZgUTjdwV0mwoaRPZs79nXTMaw/8TpEDfUmrko4WDE/fbUddPWICbCtazJSiQwz73uULoJ3gcKpjamhjpgeavhPaWdocbHyU8z6eTtva8hZCmehMU4diRFdFWQzup6aHDsfnm5+fJchaBROHdgwUgGFrPufaGGiOuTnv4Uh549YoCp+6eQyi41x6ojQm9EZI6JvHIYPeoDvMgSPwqkDfk5ivHNv16d3M0WFyMxaA4/8yxaqil/aPl4A+F2JROjN6Azh6eqLGfct6n5HQuHUmVn9nLAkvOPBmetwAV//vBayimILV8UfrYXHOHXEWprRdUYskmDu2L/DQSasZodCReHUhX8Nd8Nof/25dwfUh/DiibcgUgtn0UVLYL2EcbfMmprRtTU+Zg76eN/FdxlWg8KpC2IRg0/GeSLIWQx3aPBr2X8x8fTnYHheaJEPrKswJqRaWzO6FuF9R+C+8Il8l2FVKJy64e0gxufxnsj77V30u3SC73J4o3Ng+S6hlbU1owvy7o+H7l/MdxlWh8LJANHeMsgnzwJnx2NSWLGK7xJaWVMzOoWzN+YnPAuphBrIGYvCyUDsiAegmf0432XwhtMJ62jFGprROUidsODB5+HiyP/NBGtE4WSEpqnz0TRmMt9lWJzO0QGcVlgtX4TejE7EiJE4bgn8PIL4LsVqUTgZSf3IMmhHjOW7DIvSBnnxXUI7Qm5Gx4DBzFGPYkBgBN+lWDUKJ2OJJWh8arVdBZTWwDazlibEZnQMGEy/7xHcM+B+vkuxehROPWFnAaX1tPwqNYYQWjM6Bgym3fswTU0xEQqnnrKjgNK5CvdjIqRmdFPiFmBE2Di+y7AZwv3UWQM7CSjWQbhthoXSjG5y7HzEDXmQ7zJsCoVTb9lBQOlEwhnj1BE+m9GJGBFm3LcI9949npf3t2UUTqbwe0A1jZ3KdyVmoRPYGKe2+GpGJxXL8JdxT9M1JjOhcDIVsQTqR1dAPf9pcCLb+bayLg6AtpbvMrpl6WZ0TnJXPDJhJYaEDLPYe9ob2/lfJBBN4x9C4/K3wDmZby08S9L24f96jiEs2YzO2y0AT0xZjRDfgRZ5P3tF4WQGbMRwqNZugc4/mO9Seo31c+O7BINZohndwD5DsXjKaqNa7G7duhWRkZHw8/PD2LFjkZqaqvf42bNnMXPmTPTp0wdBQUEYP348ysvLWx+/du0a5s2bh379+iEoKAgPPvggjh492vr4F198AYVC0eGvc+fOte6nVCqRmJiIwMBA9OvXDytXroRGI9xpQBROZsL5B0O15t/QRozgu5Re0XoKo4+TIczZjI5hGCTcMwt/e3A5HOXOBj/v22+/xapVq7B8+XKkpKQgNjYWc+bMgVKpBAD88ssv+POf/4z7778fR44cwfHjx7F06VJIJH8sw5WYmAi1Wo29e/ciJSUFI0eOxLx583Djxg0AwKxZs5CTk6P3a+7cuQgNDcU999wDAGBZFomJiairq8PBgwexbds27Nu3Dy+99JIJv0umxVRVVdlfcyJL0rGQfbUFsh93811Jj1QtiIFabD2tiMskodh6vdGkr+ns4IY5Y55E/8Bwo5+bkJCA8PBwvP/++63bhg0bhhkzZmDt2rUYP348Ro8ejdWrV3f4/PLycvTv3x/79u3DmDHNy0hptVr4+vrik08+wYwZM9o9R6VSYfDgwXj22WexfPlyAMCRI0cwd+5cXLx4EUFBzfP9du3ahWeeeQa5ublwcxPeETIdOZmbSAzN/KVoWPYGdArruH5zJ1bexHcJRjF1M7pQv0H4+/R/9SiYNBoNsrKyEB8fr7c9Pj4eaWlpKC0tRXp6Ovz8/DBx4kQMHDgQkyZNwokTf/QN8/T0RFhYGHbt2oW6ujqwLItPP/0ULi4uiIuL6/B99+zZA5VKhfnz57duS09PR1hYWGswAc3BqVarkZUlzAVgKZwshI0aCdVrn6DpXusaqCf0MU4dMUUzOrFIjAeiZmDRhFVwc/Lo0WuUl5eDZVn4+Pjobffx8UFJSQny8/MBAK+//jrmz5+P3bt3495778WsWbNw8eJFAM2nk3v27MHVq1cRHBwMX19fvPHGG9i9ezf8/f07fN+kpCRMmDBB7/GSkpJ2dXh5eUEsFqOkpKRHX5+5UThZkrMr1E/+LxqWvgzOVZiTadvSsZV8l2C03jajC/QKxRNTX0bCPbMgFvW+PXHbhTM5jgPDMND9vs7hokWL8Le//Q1RUVFYs2YNYmJi8Mknn7Tuu3z5cnh6euLQoUM4duwYZsyYgYULF+LWrVvt3uvq1atIT0/Hww8/3G0d3W3nm6T7XYipsSMegCosCvJPNkJy7hTf5XSKdXMCWOtcwfhehQbKCuOeIxFJ8UD0DNwfMdkkodTZkUlZWRl8fHzg59d8xy8sLEzv8UGDBuHmzZsAgJSUFBw+fBg3btyAQtG8XFl0dDSSk5PxxRdf4IUXXtB77qefftp6R+9Ovr6+SEtL09vW2ZGdUNCRE084Nw80PrsejYtfBOcsvIuRgPWMceqIsc3ogn3646np/8LYyGkmCSYAkMlkrUFyp+TkZMTFxSE0NBQBAQHIzc3VezwvLw/Bwc3DUFSq5tNqUZuBvSKRqPXIq0VjYyN27dqF+fPnt9s/NjYWOTk5KCws1KtDLpcjOjq6d1+omdCRE8+0o8ZDGzUSsu+SIP3pOzCscBYSYP1cAQjzekR3WprR7e1mkWI3Jw88OGw2ovuPMsvpzZIlS/DEE08gJiYGcXFx2L59O4qKirBo0SIwDIOnn34ab7zxBiIiIhAZGYk9e/YgIyMDb731FoDmUPHw8MCSJUuwcuVKODo6IikpCfn5+ZgwYYLee+3duxc1NTVYsGBBuzri4+MxZMgQPPnkk1i/fj0qKyuxZs0aLFy4UJB36gAaSiAozK0CyHdugeT8Gb5LAQDUzByGBvcrfJfRYzqI8HF5GCrr25/fySQOuH/oZIwKnwiZxLz9qrZu3YpNmzahuLgYQ4YMwWuvvYZRo0a1Pr5p0yZ8/PHHqKiowODBg7FmzRo88MADrY9nZmbilVdeQWZmJrRaLQYNGoSVK1e2C6fJkyfD2dkZ33zzTYd1KJVKrFixAikpKXBwcMDs2bOxfv16yOXC7NdF4SRA4quZkH3zX4jzrvJaR+XfYqARWc8Yp45kiWNx+EZB698ZhsGwAWOQcM8suDp1veQ84ReFk4CJz56EbPc2iG/l8/L+5Y9FQqv9lZf3NhUtZNhSEgyVuh4RfWMxNnI6LTpgJSichE6ng/j8GUh/3A3JlXPd729Cpf/THzpNYfc7ChkjxW8+f4VPcDy83TseF0SEyabv1nU24bKgoKDTiZJ3TjNowXEcHnroISgUCuzdu1fvsaFDh7Z7jZdffllvn15NuBSJwN5zHxr/8Q5Ur36CpgemgZNZ5hqBNY5xaiVxhjRkLhzv+xRDIuZRMFkhm71b1zLhcuPGjRg5ciS2bt2KOXPm4MyZMwgKCkJOTo7e/gcOHMCKFSswffr0dq/1wQcfQCzu/PbyypUr8dhjj7X+3dn5j4mhLRMuPTw8cPDgQVRWVuKpp54Cx3HYsGGDUV+TLuguqBcth3rO/0B64gCkR7+DqMI8d9NYhQvAWt/ocJF7BCSBEyDxHQ1GbD2Tlkl7Nnta192Ey7ZmzpzZOlXgTpmZmViwYAGOHz+OgQMHIikpSW+y5dChQ7F48WI8/fTTHdZh1gmXOhbisych/fkIxJcywDSZrv2FOjwUVcOLTfZ65sTIPCDx/xMkgeMhcqLrSbbCJo+cWiZctg2MlgmXbeXn5+PEiRP49NNP9bbX1tbisccew7vvvtvlKNrNmzfjnXfeQZ8+fTBz5kw888wzkMlkALqfcNky07xHRGKwIx4AO+IBoFEFyfkzEGekQHLhDBh172bma31dAQg4nKTukHjHQewzCmLP4WBMNHCSCIdNhlN3Ey7b2rFjB7y8vDB5sv5S48uWLUNCQgLGj++8ef0TTzyByMhIeHp64ty5c3j55ZdRUFCAzZs3A7DghEsHJ2jj4qGNi4dao4b4Qjokv5yAJOs0mAbjp6CwHjLT1WYijFMfSLzvhdj7Xojch4Axc2M5wi+bDKcWnU24vJNWq8WXX36JefPmQSr9owf1zp07cenSpXZTD9paunRp658jIiLg6uqKRYsWYd26dfD09Oywjs7qMxmZHOzw0WCHj4Za2wTxrxchunYZ4muXIb52BUx9TbcvwTrzPxmUkftApAiH2P1uiD3ugcjZ+juLEsPZZDh1N+HyTocOHUJRUREWLlyot/3EiRPIzs5Gnz76s9sXLVqE2NhYHD58uMP3jomJAQBcv34dnp6e/E+4lEjB3j0M7N3D0AQAHAemSNkcVLmXIcq7DFFhARhOf56WTq4BLNnKSSSHyDkEIrfBELvfDZEiAiIHYU5IJZZhk+F054TLmTNntm5PTk5udzdux44dGDVqFAYMGKC3ffXq1e2uWd1333145ZVXMGXKlE7fu6UPT8uM89jYWLz99tsoLCxsDTpeJ1wyDLiAEGgDQqAdPal5W0M9RLd+g6j4JpjiQoiKbwJOWjCNleA0FUCb4OoxsRMYmQKM3Bsipz5gHAMhcgqCyDkUjKM/naYRPTYZTkDXEy5bKJVKHDt2DB999FG75wcGBiIwMLDd9qCgIPTt2xdA88XujIwMjB49Gm5ubsjMzMSLL76ISZMmtc4qt4oJl47O0PUfAl3/Ia2bWkZScTq2OaC09eBYFTitCmj5XasCx2nBMGKAEQOM6PffxQAjASN1aw4jmQKMVAFGLLzrWES4bDacZs2ahYqKCmzYsKF1wuXXX3+NkJA/lg/67LPP4Obm1uHYJkPIZDLs2bMHb775JjQaDYKDg7Fw4UI8++yzrfuIxWLs2rULK1aswMSJE/UmXFoDRiQG4+ADgE6xiGXZ7DgnQoh1o5N8Iji9nXb06aefYurUqQgJCYFCoUBBQYHe6588ebLT1/nuu+9a96uqqsLixYsREhKCkJAQLF68GFVVwl6a3ZZQOBFB6Wqdt5ZpR3f+2rhxIxiG0Ts1V6lUiI+Px6pVqzp8j7i4uHavs2zZMri4uOi1t3388cdx4cIFfPPNN9i9ezcuXLiAJ554wuzfA9KMTuuIoJhq2hHQPPVo3LhxOH/+PEJDu14uavjw4Rg1ahQ2bdoEAMjJyUFcXBwOHz6MkSNHAgBOnz6NSZMmISMjAwMH0lLk5kZHTkQwulvnra2WaUePPPJIr9735MmTuHbtmt7rpKent1sbbuTIkXB2du6wFmJ6FE5EMEw17chYSUlJiIiIaF26G2ieduTl5aU3ip9hGHh7ewt2nTdbQ+FEBKc3046MVVlZif3793d49NXR9KKOaiHmQeFEBMMU046M9eWXX0IkEmHOnDl62319fVFWVgaO++OSLMdxKC8vF+w6b7aGwokIRnfrvN2ps2lHxvrss88wc+ZMuLvrr8AcGxuLuro6pKent25LT09HfX19u1qIedjsCHFinXo77QgAiouLUVxcjGvXrgFovvNWXV2N4OBgeHh4tO53+vRpZGdn47333mv3GmFhYXjwwQfx/PPPY9OmTeA4Ds8//zwmTJhAd+oshMKJCIopph1t374db775Zuvf586dCwD48MMPMX/+/NbtSUlJCAsLax0q0NbHH3+Mf/zjH5g1axYAYNKkSa2LXRLzo3FOhBBBomtOhBBBonAihAgShRMhRJAonAghgkThRAgRJAonQoggUTgRQgSJwokQIkgUToQQQaJwIoQIEoUTIUSQKJwIIYJE4UQIESQKJ0KIIFE4EUIEicKJECJIFE6EEEH6f+Kp0sjeeg/VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#也可以用饼形图显示数据\n",
    "salary_ranges['Grade'].value_counts().sort_values(ascending=False).head(5).plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Top 20 Grade by Mean Biweekly High Rate')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看那个工资最高，用条形图显示，是定比等级数据\n",
    "salary_ranges.groupby('Grade')[['Biweekly High Rate']].mean().sort_values(\n",
    "    'Biweekly High Rate', ascending=False).head(20).plot.bar(stacked=False, ax=ax, color='darkorange')\n",
    "ax.set_title('Top 20 Grade by Mean Biweekly High Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 20 Grade by Mean Biweekly High Rate')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.gca()\n",
    "\n",
    "salary_ranges.groupby('Grade')[['Biweekly High Rate']].mean().sort_values(\n",
    "    'Biweekly High Rate', ascending=False).head(20).plot.bar(stacked=False, ax=ax, color='darkorange')\n",
    "ax.set_title('Top 20 Grade by Mean Biweekly High Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Bottom 20 Grade by Mean Biweekly High Rate')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看哪些工资最低\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.gca()\n",
    "\n",
    "salary_ranges.groupby('Grade')[['Biweekly High Rate']].mean().sort_values(\n",
    "    'Biweekly High Rate', ascending=False).tail(20).plot.bar(stacked=False, ax=ax, color='darkorange')\n",
    "ax.set_title('Bottom 20 Grade by Mean Biweekly High Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.931919540229886"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们可以观察一下工资最高与工资最低相差的比值\n",
    "sorted_df = salary_ranges.groupby('Grade')[['Biweekly High Rate']].mean().sort_values(\n",
    "    'Biweekly High Rate', ascending=False)\n",
    "sorted_df.iloc[0][0] / sorted_df.iloc[-1][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到最高工资与最低工资相差13倍之多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 多少人喜欢旧金山国际机场数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = pd.read_csv('~/开发/AI相关/特征工程/Feature-Engineering-Made-Easy-master/data/2013_SFO_Customer_survey.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3535, 95)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3535 entries, 0 to 3534\n",
      "Data columns (total 95 columns):\n",
      "RESPNUM            3535 non-null int64\n",
      "CCGID              3535 non-null object\n",
      "RUN                3535 non-null int64\n",
      "INTDATE            3535 non-null int64\n",
      "GATE               3535 non-null int64\n",
      "STRATA             3535 non-null int64\n",
      "PEAK               3535 non-null int64\n",
      "METHOD             3535 non-null int64\n",
      "AIRLINE            3535 non-null int64\n",
      "FLIGHT             3535 non-null int64\n",
      "DEST               3535 non-null int64\n",
      "DESTGEO            3535 non-null int64\n",
      "DESTMARK           3535 non-null int64\n",
      "ARRTIME            3535 non-null object\n",
      "DEPTIME            3535 non-null object\n",
      "Q2PURP1            3535 non-null int64\n",
      "Q2PURP2            3459 non-null float64\n",
      "Q2PURP3            163 non-null float64\n",
      "Q2PURP4            17 non-null float64\n",
      "Q2PURP5            2 non-null float64\n",
      "Q2PURP6            0 non-null float64\n",
      "Q3GETTO1           3535 non-null int64\n",
      "Q3GETTO2           3505 non-null float64\n",
      "Q3GETTO3           17 non-null float64\n",
      "Q3GETTO4           1 non-null float64\n",
      "Q3GETTO5           0 non-null float64\n",
      "Q3GETTO6           0 non-null float64\n",
      "Q3PARK             284 non-null float64\n",
      "Q4BAGS             3535 non-null int64\n",
      "Q4BUY              3535 non-null int64\n",
      "Q4FOOD             3535 non-null int64\n",
      "Q4WIFI             3535 non-null int64\n",
      "Q5FLYPERYR         3535 non-null int64\n",
      "Q6TENURE           3535 non-null float64\n",
      "SAQ                3535 non-null int64\n",
      "Q7A_ART            3535 non-null int64\n",
      "Q7B_FOOD           3535 non-null int64\n",
      "Q7C_SHOPS          3535 non-null int64\n",
      "Q7D_SIGNS          3535 non-null int64\n",
      "Q7E_WALK           3535 non-null int64\n",
      "Q7F_SCREENS        3535 non-null int64\n",
      "Q7G_INFOARR        3535 non-null int64\n",
      "Q7H_INFODEP        3535 non-null int64\n",
      "Q7I_WIFI           3535 non-null int64\n",
      "Q7J_ROAD           3535 non-null int64\n",
      "Q7K_PARK           3535 non-null int64\n",
      "Q7L_AIRTRAIN       3535 non-null int64\n",
      "Q7M_LTPARK         3535 non-null int64\n",
      "Q7N_RENTAL         3535 non-null int64\n",
      "Q7O_WHOLE          3535 non-null int64\n",
      "Q8COM1             1495 non-null float64\n",
      "Q8COM2             308 non-null float64\n",
      "Q8COM3             59 non-null float64\n",
      "Q9A_CLNBOARD       3535 non-null int64\n",
      "Q9B_CLNAIRTRAIN    3535 non-null int64\n",
      "Q9C_CLNRENT        3535 non-null int64\n",
      "Q9D_CLNFOOD        3535 non-null int64\n",
      "Q9E_CLNBATH        3535 non-null int64\n",
      "Q9F_CLNWHOLE       3535 non-null int64\n",
      "Q9COM1             180 non-null float64\n",
      "Q9COM2             4 non-null float64\n",
      "Q9COM3             4 non-null float64\n",
      "Q10SAFE            3535 non-null int64\n",
      "Q10COM1            1683 non-null float64\n",
      "Q10COM2            221 non-null float64\n",
      "Q10COM3            14 non-null float64\n",
      "Q11A_USEWEB        3535 non-null int64\n",
      "Q11B_USESFOAPP     3535 non-null int64\n",
      "Q11C_USEOTHAPP     3535 non-null int64\n",
      "Q11D_USESOCMED     3535 non-null int64\n",
      "Q11E_USEWIFI       3535 non-null int64\n",
      "Q12COM1            706 non-null float64\n",
      "Q12COM2            75 non-null float64\n",
      "Q12COM3            2 non-null float64\n",
      "Q13_WHEREDEPART    3535 non-null int64\n",
      "Q13_RATEGETTO      3535 non-null int64\n",
      "Q14A_FIND          3535 non-null int64\n",
      "Q14B_SECURITY      3535 non-null int64\n",
      "Q15_PROBLEMS       3535 non-null int64\n",
      "Q15COM1            389 non-null float64\n",
      "Q15COM2            21 non-null float64\n",
      "Q15COM3            1 non-null float64\n",
      "Q16_REGION         3535 non-null int64\n",
      "Q17_CITY           3234 non-null object\n",
      "Q17_ZIP            2276 non-null float64\n",
      "Q17_COUNTRY        3410 non-null object\n",
      "HOME               3535 non-null int64\n",
      "Q18_AGE            3535 non-null int64\n",
      "Q19_SEX            3535 non-null int64\n",
      "Q20_INCOME         3535 non-null int64\n",
      "Q21_HIFLYER        3535 non-null int64\n",
      "Q22A_USESJC        3535 non-null int64\n",
      "Q22B_USEOAK        3535 non-null int64\n",
      "LANG               3535 non-null int64\n",
      "WEIGHT             3535 non-null float64\n",
      "dtypes: float64(29), int64(61), object(5)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "customer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到这个数据集中包含很多数据，在这里我们只关心`Q7A_ART`这列数据，这列数据是关于艺术品和展览的，有几个值\n",
    "\n",
    "- 1: 不可接受\n",
    "- 2: 低于平均\n",
    "- 3: 平均\n",
    "- 4: 不错\n",
    "- 5: 特别好\n",
    "- 6: 从未有人使用或参观过\n",
    "- 0: 空"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RESPNUM               0\n",
       "CCGID                 0\n",
       "RUN                   0\n",
       "INTDATE               0\n",
       "GATE                  0\n",
       "STRATA                0\n",
       "PEAK                  0\n",
       "METHOD                0\n",
       "AIRLINE               0\n",
       "FLIGHT                0\n",
       "DEST                  0\n",
       "DESTGEO               0\n",
       "DESTMARK              0\n",
       "ARRTIME               0\n",
       "DEPTIME               0\n",
       "Q2PURP1               0\n",
       "Q2PURP2              76\n",
       "Q2PURP3            3372\n",
       "Q2PURP4            3518\n",
       "Q2PURP5            3533\n",
       "Q2PURP6            3535\n",
       "Q3GETTO1              0\n",
       "Q3GETTO2             30\n",
       "Q3GETTO3           3518\n",
       "Q3GETTO4           3534\n",
       "Q3GETTO5           3535\n",
       "Q3GETTO6           3535\n",
       "Q3PARK             3251\n",
       "Q4BAGS                0\n",
       "Q4BUY                 0\n",
       "                   ... \n",
       "Q10COM3            3521\n",
       "Q11A_USEWEB           0\n",
       "Q11B_USESFOAPP        0\n",
       "Q11C_USEOTHAPP        0\n",
       "Q11D_USESOCMED        0\n",
       "Q11E_USEWIFI          0\n",
       "Q12COM1            2829\n",
       "Q12COM2            3460\n",
       "Q12COM3            3533\n",
       "Q13_WHEREDEPART       0\n",
       "Q13_RATEGETTO         0\n",
       "Q14A_FIND             0\n",
       "Q14B_SECURITY         0\n",
       "Q15_PROBLEMS          0\n",
       "Q15COM1            3146\n",
       "Q15COM2            3514\n",
       "Q15COM3            3534\n",
       "Q16_REGION            0\n",
       "Q17_CITY            301\n",
       "Q17_ZIP            1259\n",
       "Q17_COUNTRY         125\n",
       "HOME                  0\n",
       "Q18_AGE               0\n",
       "Q19_SEX               0\n",
       "Q20_INCOME            0\n",
       "Q21_HIFLYER           0\n",
       "Q22A_USESJC           0\n",
       "Q22B_USEOAK           0\n",
       "LANG                  0\n",
       "WEIGHT                0\n",
       "Length: 95, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customre.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2656\n",
       "unique       5\n",
       "top          4\n",
       "freq      1066\n",
       "Name: Q7A_ART, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "art_ratings = customer['Q7A_ART']\n",
    "#这列数据是一个定型数据，没个数字代表不同类型\n",
    "#如果我们把其中0和6去掉，更类似于一个评分的排序\n",
    "art_ratings = art_ratings[(art_ratings>=1) & (art_ratings<=5)]\n",
    "#将数值转换为字符串\n",
    "art_ratings = art_ratings.astype(str)\n",
    "art_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1604c208>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用饼形图来显示一下数据，定序数据\n",
    "art_ratings.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a160964a8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用条形图显示数据\n",
    "art_ratings.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a162b9828>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gsZAElt/vV19fn2655RZn7PTp03r33Xf1/PPP67333pN0dhU1ZcoUp6a3t9dZdcXGxqq3tzcooGzbVl9f35CV2bkudnHAZdN8LKzvvY6OjvC+18N8fcBwhOQeVlZWlt599101NTU5PzfddJPuueceNTU1acaMGfJ4PGpsbHRec+rUKbW0tDj3rJKTk9Xf3y+/3+/U+P1+DQwMBN3XAgBcmUKywoqOjg56KlCSIiMjde2112r27NmSpOXLl2v9+vXy+XyaMWOG1q1bp6ioKOXm5kqSEhISlJ6eruLiYlVVVcm2bRUXFysjI4N/6QEAQvfQxX+yYsUKDQ4OqqSkRJZlKSkpSTt37tSECROcms2bN6u0tFQLFy6UJGVmZqqioiJcLQIARjGXZVn2SDcBmCb6hWOy8iaHbb5w38MK9/UBw8FnCQIAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIxAYAEAjEBgAQCMQGABAIwQksB66qmndPvtt2vq1KmaPn26Fi9erP379wfV2LatsrIyJSYmKi4uTllZWWprawuqsSxLBQUF8nq98nq9KigokGVZoWgRAGC4kARWc3Ozli1bpt27d6uhoUHjxo3TggULdPz4caemqqpKGzduVHl5ufbu3Su3262cnBydOHHCqcnPz1cgEFBdXZ3q6+sVCARUWFgYihYBAIZzWZZlh/qk/f398nq92rZtmzIzM2XbthITE3X//fdr1apVkqTBwUH5fD49/vjjysvLU3t7u1JSUrRr1y6lpqZKklpaWpSZmal9+/bJ5/OFuk3gvxb9wjFZeZPDNl9HR0dY/w6E+/qA4bgs97D6+/t15swZRUdHS5I6OzvV3d2ttLQ0pyYiIkLz5s1Ta2urJMnv92v8+PFKSUlxalJTUxUVFeXUAACuXOMux0kffvhh3XDDDUpOTpYkdXd3S5LcbndQndvtVldXlySpp6dHMTExcrlcznGXy6WJEyeqp6fngnN1dHSEun1gGCLD/t4L73zhvz5A0kV3EkIeWI888ojee+897dq1S2PHjg06dm4YSWcfxDg/oM53fs352CrEiGg+Ftb3Xri3BMN9fcBwhHRLcPXq1Xr11VfV0NCgadOmOeMej0eShqyUent7nVVXbGysent7Zdv/f0vNtm319fUNWZkBAK48IQus0tJS1dfXq6GhQTNnzgw6Fh8fL4/Ho8bGRmfs1KlTamlpce5ZJScnq7+/X36/36nx+/0aGBgIuq8FALgyhWRLcNWqVXrllVe0detWRUdHO/esoqKiNH78eLlcLi1fvlzr16+Xz+fTjBkztG7dOkVFRSk3N1eSlJCQoPT0dBUXF6uqqkq2bau4uFgZGRlsTQAAQhNYNTU1kqTs7Oyg8dLSUq1evVqStGLFCg0ODqqkpESWZSkpKUk7d+7UhAkTnPrNmzertLRUCxculCRlZmaqoqIiFC0CAAx3WX4PC/im4/ewgPDjswQBAEYgsAAARiCwAABGILAAAEYgsAAARiCwAABGILAAAEYgsAAARiCwAABGILAAAEYgsAAARiCwAABGILAAAEYgsAAARiCwAABGILAAAEYgsAAARiCwAABGILAAAEYYN9INAKaKfuFYGGeLlJrDN1/0Va6wzQUMl8uyLHukmwBwcdEvHJOVN3mk2wBGFFuCAAAjEFgAACMQWAAAIxBYAAAjEFgAACMQWAAAIxBYAAAjEFgAACOMusCqqanR3Llz5fF4NH/+fL377rsj3RIAYBQYVZ90sXPnThUUFGj9+vVKTU1VTU2Ntm/frvfee09Tp04d6faAr62srEzl5eVhm6+0tFSrV68O23zA5TSqAuuOO+7QnDlz9PTTTztjN998s7Kzs/Xoo4+OYGfAyOro6JDP5xvpNoARNWq2BD///HP99a9/VVpaWtB4WlqaWltbR6grAMBoMWoCq6+vT6dPn5bb7Q4ad7vd6unpGaGuAACjxaj7ehGXK/hrDWzbHjJ2ro6OjsvdEjAq8F7HleBiW9+jJrBiYmI0duzYIaup3t7eIauuc7GvjysB97CAUbQleNVVV+nGG29UY2Nj0HhjY6NSUlJGqCsAwGgxalZYkvTzn/9chYWFSkpKUkpKip5//nl98sknysvLG+nWAAAjbFQF1sKFC/Xpp59q7dq16u7u1qxZs7Rjxw55vd6Rbg0AMMJG1e9hAQBwIaPmHhYAABdDYAEAjEBgAQCMQGABAIxAYAEAjEBgAZfg73//u1asWKHZs2fL7XZr1qxZ+tWvfqVjx445NTfccIOio6OH/CxatGjI+f72t7/puuuuU0ZGxn/d00MPPaTrrrtOW7ZsGXJs27ZtQT34fD4tXrxYbW1tkqQvvvjiK3s99+eXv/zlf90bEEoEFjBMhw8f1u233662tjZVV1fr/fff16ZNm3TgwAGlpaWps7NT0tlPZ2lvb3d+3nnnHblcLi1YsGDIOWtra7Vs2TK1tbWpvb39knv67LPPVFdXp+LiYtXW1n5lTWRkpNrb23XgwAHt2LFDJ0+e1KJFi/T5559r3LhxQb1u2LBBY8eODRp78sknL7kv4HIgsIBhKikp0ZgxY/Taa69p/vz5mjp1qn7wgx/otdde05gxY1RSUiJJmjhxojwej/Pz1ltvacKECUMCa3BwUHV1dVq6dKl+8pOf6KWXXrrknv74xz/K6/XqwQcf1MGDB7V///4hNS6XSx6PR3FxcbrppptUVFSkI0eOOB+me26vV1999QXHgJFGYAHDcPz4ce3Zs0f5+fmKjIwMOhYZGally5bprbfekmVZQcds29ZLL72kxYsXD3ndH/7wB02dOlXXX3+9Fi9erJdffln//ve/L6mv2tpaLVq0SJGRkbrrrrsuuMr6kmVZqq+vlyR961vfuqS5gJFGYAHD8PHHH8u2bc2cOfMrjyckJMi2bX388cdB442Njers7NS999475DW1tbVasmSJJOnWW29VRESE3njjjWH3dPjwYbW0tCg3N1eStGTJEu3YsUOfffZZUN3AwIAmT56sSZMmadq0aXr11VeVmZl5wWsBRisCC7gEF/puNts++wln569atmzZoptvvllz584NGj906JBaW1udsHG5XFq0aNElbQtu3bpV8+fPl8fjkSTddtttioyM1Ouvvx5UFxkZqaamJr399tuqrKzU9OnTVVlZOex5gNFiVH34LTBaTZ8+XS6XSwcOHNBdd9015PjBgwc1btw4xcfHO2P/+Mc/9MYbb2jdunVD6mtra3X69Gldf/31ztiXoXf06FFNmTLlov2cPn1a27dvV1dXl2JiYpzxM2fOqLa2VgsXLnTGXC6Xvve970mSZs6cqU8++UTLli3Tn/70p2FePTA6sMIChuHaa69Venq6nnvuOZ08eTLo2MmTJ1VTU6O77rpL11xzjTO+fft2ffvb3w4KD+nso+S///3v9eijj6qpqcn5aW5u1pw5c7Rt27b/2M+ePXv06aefqrGxMegcr7zyit555x3nicWvUlRUpEAgoIaGhkv8vwCMLAILGKa1a9fq9OnTWrBggd555x0dPXpUTU1NysnJ0bhx41ReXu7U2rbtrHQmTJgQdJ7du3err69PS5cu1ezZs4N+7rnnHm3dulVnzpy5aC+1tbVKT0/XjTfeGPT6H/3oR/L5fNq6desFX3v11Vfr3nvv1Zo1a/7jPMBoQmABwzRt2jQ1NjYqMTFRDzzwgObOnau7775bY8aMUXNzs3MvSZKampr08ccfa+nSpUPO89JLL+m2227TddddN+TYggULdOTIEb399tsX7KOnp0e7d+9Wdnb2Vx7Pzs7W9u3bLxpGDzzwgA4ePKhXX331IlcMjC58HxbwNWzatEm//vWv9eKLLyorK2uk2wG+0XjoAvgaCgsLFRsbq/b2dqWlpSkiImKkWwK+sVhhAaPQ+vXr9dRTT33lsVtuucX55V/gSkJgAaPQ8ePHdfz48a889p3vfEeTJk0Kc0fAyCOwAABG4ClBAIARCCwAgBEILACAEQgsAIARCCwAgBH+D+MAUHXRppS7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以用箱型图可视化数据\n",
    "art_ratings.value_counts().plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 气候变化数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "climate = pd.read_csv('~/开发/AI相关/特征工程/Feature-Engineering-Made-Easy-master/data/GlobalLandTemperaturesByCity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1743-11-01</td>\n",
       "      <td>6.068</td>\n",
       "      <td>1.737</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1743-12-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1744-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1744-02-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1744-03-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dt  AverageTemperature  AverageTemperatureUncertainty   City  \\\n",
       "0  1743-11-01               6.068                          1.737  Århus   \n",
       "1  1743-12-01                 NaN                            NaN  Århus   \n",
       "2  1744-01-01                 NaN                            NaN  Århus   \n",
       "3  1744-02-01                 NaN                            NaN  Århus   \n",
       "4  1744-03-01                 NaN                            NaN  Århus   \n",
       "\n",
       "   Country Latitude Longitude  \n",
       "0  Denmark   57.05N    10.33E  \n",
       "1  Denmark   57.05N    10.33E  \n",
       "2  Denmark   57.05N    10.33E  \n",
       "3  Denmark   57.05N    10.33E  \n",
       "4  Denmark   57.05N    10.33E  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8599212 entries, 0 to 8599211\n",
      "Data columns (total 7 columns):\n",
      "dt                               object\n",
      "AverageTemperature               float64\n",
      "AverageTemperatureUncertainty    float64\n",
      "City                             object\n",
      "Country                          object\n",
      "Latitude                         object\n",
      "Longitude                        object\n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 459.2+ MB\n"
     ]
    }
   ],
   "source": [
    "climate.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                    0\n",
       "AverageTemperature               364130\n",
       "AverageTemperatureUncertainty    364130\n",
       "City                                  0\n",
       "Country                               0\n",
       "Latitude                              0\n",
       "Longitude                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climate.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dt</th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1743-11-01</td>\n",
       "      <td>6.068</td>\n",
       "      <td>1.737</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1744-04-01</td>\n",
       "      <td>5.788</td>\n",
       "      <td>3.624</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1744-05-01</td>\n",
       "      <td>10.644</td>\n",
       "      <td>1.283</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1744-06-01</td>\n",
       "      <td>14.051</td>\n",
       "      <td>1.347</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1744-07-01</td>\n",
       "      <td>16.082</td>\n",
       "      <td>1.396</td>\n",
       "      <td>Århus</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>57.05N</td>\n",
       "      <td>10.33E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dt  AverageTemperature  AverageTemperatureUncertainty   City  \\\n",
       "0  1743-11-01               6.068                          1.737  Århus   \n",
       "5  1744-04-01               5.788                          3.624  Århus   \n",
       "6  1744-05-01              10.644                          1.283  Århus   \n",
       "7  1744-06-01              14.051                          1.347  Århus   \n",
       "8  1744-07-01              16.082                          1.396  Århus   \n",
       "\n",
       "   Country Latitude Longitude  \n",
       "0  Denmark   57.05N    10.33E  \n",
       "5  Denmark   57.05N    10.33E  \n",
       "6  Denmark   57.05N    10.33E  \n",
       "7  Denmark   57.05N    10.33E  \n",
       "8  Denmark   57.05N    10.33E  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去掉缺失值\n",
    "climate.dropna(axis=0, inplace=True)\n",
    "climate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.235082e+06</td>\n",
       "      <td>8.235082e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.672743e+01</td>\n",
       "      <td>1.028575e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.035344e+01</td>\n",
       "      <td>1.129733e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.270400e+01</td>\n",
       "      <td>3.400000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.029900e+01</td>\n",
       "      <td>3.370000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.883100e+01</td>\n",
       "      <td>5.910000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.521000e+01</td>\n",
       "      <td>1.349000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.965100e+01</td>\n",
       "      <td>1.539600e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AverageTemperature  AverageTemperatureUncertainty\n",
       "count        8.235082e+06                   8.235082e+06\n",
       "mean         1.672743e+01                   1.028575e+00\n",
       "std          1.035344e+01                   1.129733e+00\n",
       "min         -4.270400e+01                   3.400000e-02\n",
       "25%          1.029900e+01                   3.370000e-01\n",
       "50%          1.883100e+01                   5.910000e-01\n",
       "75%          2.521000e+01                   1.349000e+00\n",
       "max          3.965100e+01                   1.539600e+01"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "climate.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们关注的是AverageTemperature列。温度数据属于定量数据属于定距数据，我们没法显示所有温度值，因为数据量过大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111994"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#显示AverageTemperature中独特数值的数量\n",
    "climate['AverageTemperature'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20f822b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用直方图显示数据\n",
    "climate['AverageTemperature'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将dt列数据转换为日期，并取年份\n",
    "climate['dt'] = pd.to_datetime(climate['dt'])\n",
    "climate['year'] = climate['dt'].map(lambda value: value.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/super/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#只看美国的数据\n",
    "climate_sub_us = climate.loc[climate['Country'] == 'United States']\n",
    "#并将年份转换为世纪,1983会变成20\n",
    "climate_sub_us['century'] = climate_sub_us['year'].map(lambda x: int(x/100+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23ef06d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a251b8e10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a22de8080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a23552cc0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对每个世纪显示直方图\n",
    "climate_sub_us['AverageTemperature'].hist(by=climate_sub_us['century'], \n",
    "                                          sharex=True, sharey=True, \n",
    "                                          figsize=(10, 10), \n",
    "                                          bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2185a128>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图表示\n",
    "climate_sub_us.groupby('century')['AverageTemperature'].mean().plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "差值在定距等级中有意义的，所以我们可以回答美国从18世纪至今温度上升多少的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把世纪温度的变化数据存储到Pandas的Series对象中\n",
    "century_changes = climate_sub_us.groupby('century')['AverageTemperature'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "century\n",
       "18    12.073243\n",
       "19    13.662870\n",
       "20    14.386622\n",
       "21    15.197692\n",
       "Name: AverageTemperature, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "century_changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.124449115460754"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用21世纪的温度数据减去18世纪的温度数据\n",
    "century_changes[21] - century_changes[18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按年显示温度的变化\n",
    "x = climate_sub_us['year']\n",
    "y = climate_sub_us['AverageTemperature']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.scatter(x, y)\n",
    "plt.show()\n",
    "#但是显示效果并不好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2678d2e8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#我们使用groupby清除年份数据中的噪声\n",
    "climate_sub_us.groupby('year').mean()['AverageTemperature'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26715f28>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用滑动均值平滑图像\n",
    "climate_sub_us.groupby('year').mean()['AverageTemperature'].rolling(10).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
